Transcriptome wide analysis of natural antisense transcripts shows potential role in breast cancer Stephane Wenric, Sonia El Guendi, Jean-Hubert Caberg, Warda Bezzaou, Corinne Fasquelle, Benoit Charloteaux, Latifa Karim, Benoit Hennuy, Pierre Frères, Joëlle Collignon, Meriem Boukerroucha, Hélène Schroeder, Fabrice Olivier, Véronique Jossa, Guy Jérusalem, Claire Josse, Vincent Bours Why breast cancer? Why antisense lncRNAs? ● Most frequent cancer type in women ( ∼35% of female cancers) ● First cause of cancer death in women (∼35% of cancer deaths) ● ⅛ women will have breast cancer during their lifetime ● Breast cancer involves several genes ● Genetic alteration mechanisms not always well known ● Some of these mechanisms involve antisense lncRNAs ● Long non-coding RNAs (lncRNAs) = non protein-coding transcripts longer than 200 nt ● Antisense lncRNA or natural antisense transcript (NAT) = lncRNA ○ Sharing the same genomic location as a protein-coding gene ○ Transcribed in the opposite direction ○ Overlapping > 1 exon ● NATs ○ Regulate protein-coding gene expression ○ Overlap more than 50% of sense RNA transcripts ○ Have a lower expression than protein-coding genes ○ Can have an effect in cis or in trans Study design Validation 23 ER+/HER- breast cancer patients ○ Tumors ○ Adjacent healthy tissue DNA RNA Strand-specific RNA-Seq CGH ○ reads alignment ○ QC ○ gene quantification ○ normalization Validation ○ RNA-Seq fold-changes vs. CGH (all genes) ○ RNA-Seq fold-changes vs. microarray fold-changes (external study) ○ RNA-Seq vs. RT-qPCR (subset of genes) ○ Principal Components plot (tumors vs. healthy) Results RNA-Seq vs. CGH: Overall expression levels of coding gene transcripts inside genomic amplification or deletion newly acquired in the tumor were respectively increased and decreased. RNA-Seq PCA: Appropriate separation between the sample classes. RNA-Seq vs microarray: Comparison with GEO Dataset GSE65216 ● Average Spearman correlation: 0.613 (p-val < 0.001) ● 76.6% of genes modulated in the same direction RNA-Seq vs RT-qPCR: Small set of genes (protein coding and antisenses) Differential expression analysis (DESeq2) Differential correlation analysis (DGCA) varRatio analysis: RT-qPCR External microarray data Lists of antisenses / protein-coding pairs Survival analysis TCGA Data 1066 RNA-Seq Breast Cancer samples p-val < 0.05 p-val < 0.05 extreme values RNA-Seq pipeline Validation of RNA-Seq data Antisenses selection methods Filtering of significant / extreme antisenses Test for enrichment in survival-associated genes Conclusion This is the first breast cancer-based, transcriptome-wide, strand-specific RNA-Seq study performed with paired tumor and adjacent tissue samples. Our results show that opposite strand transcription regulation might play a key role in the breast cancer disease, involving several different protein-coding genes and antisenses. Further functional molecular studies will be needed to explore the mechanisms and roles of specific antisenses. Global over-expression of antisenses in breast cancer tumors Highlighting of pairs of antisense/protein coding genes linked to survival Disruption of positive correlations between antisenses and protein-coding genes in breast cancer tumors Modifications of NAT/PCT correlations between healthy samples and tumors Funding: French Community of Belgium, Belgian Funds for Scientific Research (F.R.S.-FNRS), F.R.S.-FNRS-Télévie, CHU Liège (F.I.R.S.), Wallonia (XSPRELTRIN), CÉCI (F.R.S.-FNRS Grant 2.5020.11)

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Page 1: Transcriptome wide analysis of natural antisense transcripts … 2017... · 2019-12-23 · Transcriptome wide analysis of natural antisense transcripts shows potential role in breast

Transcriptome wide analysis of natural antisense transcripts shows potential role in breast cancer

Stephane Wenric, Sonia El Guendi, Jean-Hubert Caberg, Warda Bezzaou, Corinne Fasquelle, Benoit Charloteaux, Latifa Karim, Benoit Hennuy, Pierre Frères, Joëlle Collignon, Meriem Boukerroucha, Hélène Schroeder, Fabrice Olivier, Véronique Jossa, Guy Jérusalem, Claire Josse, Vincent Bours

Why breast cancer? Why antisense lncRNAs?

● Most frequent cancer type in women( ∼35% of female cancers)

● First cause of cancer death in women(∼35% of cancer deaths)

● ⅛ women will have breast cancer duringtheir lifetime

● Breast cancer involves several genes● Genetic alteration mechanisms not always well known● Some of these mechanisms involve antisense lncRNAs

● Long non-coding RNAs (lncRNAs) = non protein-coding transcripts longer than 200 nt

● Antisense lncRNA or natural antisense transcript (NAT) = lncRNA○ Sharing the same genomic location as a protein-coding gene○ Transcribed in the opposite direction○ Overlapping > 1 exon

● NATs○ Regulate protein-coding gene expression○ Overlap more than 50% of sense RNA transcripts○ Have a lower expression than protein-coding genes○ Can have an effect in cis or in trans

Study design

Validation

23 ER+/HER- breast cancer

patients

○ Tumors○ Adjacent healthy tissue

DNA

RNA

Strand-specificRNA-Seq

CGH

○ reads alignment○ QC○ gene quantification○ normalization

Validation○ RNA-Seq fold-changes vs. CGH (all genes)○ RNA-Seq fold-changes vs. microarray

fold-changes (external study)○ RNA-Seq vs. RT-qPCR

(subset of genes)○ Principal Components plot (tumors vs.

healthy)

ResultsRNA-Seq vs. CGH:Overall expression levels of coding gene transcripts inside genomic amplification or deletion newly acquired in the tumor were respectively increased and decreased.

RNA-Seq PCA:Appropriate separation between the sample classes.

RNA-Seq vs microarray:Comparison with GEO Dataset GSE65216● Average Spearman correlation: 0.613 (p-val < 0.001)● 76.6% of genes modulated in the same direction

RNA-Seq vs RT-qPCR:Small set of genes (protein coding and antisenses)

Differential expression analysis (DESeq2)

Differential correlation analysis (DGCA)

varRatio analysis:

RT-qPCRExternal

microarray data

Lists of antisenses / protein-coding

pairs

Survival analysis

TCGAData

1066 RNA-Seq Breast Cancer

samples

p-val

< 0.05

p-val

< 0.05ex

treme v

alues

RNA-Seq pipeline Validation of RNA-Seq data Antisenses selection methods Filtering of significant / extreme antisenses

Test for enrichment in survival-associated genes

ConclusionThis is the first breast cancer-based, transcriptome-wide, strand-specific RNA-Seq study performed with paired tumor and adjacent tissue samples. Our results show that opposite strand transcription regulation might play a key role in the breast cancer disease, involving several different protein-coding genes and antisenses. Further functional molecular studies will be needed to explore the mechanisms and roles of specific antisenses.

Global over-expression of antisenses in breast cancer tumors

Highlighting of pairs of antisense/protein coding genes linked to survival

Disruption of positive correlations between antisenses and protein-coding genes in breast cancer tumors

Modifications of NAT/PCT correlations between healthy samples and tumors

Funding: French Community of Belgium, Belgian Funds for Scientific Research (F.R.S.-FNRS), F.R.S.-FNRS-Télévie, CHU Liège (F.I.R.S.), Wallonia (XSPRELTRIN), CÉCI (F.R.S.-FNRS Grant 2.5020.11)